Abstract Colon cancer is the third leading cause of cancer-related deaths in the United States in both men and women. A major clinical challenge is to obtain an effective treatment strategy for each patient or at least identify a subset of patients who could bene?t from a particular treatment. Since each colon cancer has its own unique features, it is very important to obtain personalized cancer treatments and ?nd a way to tailor treatment strategies for each patient based on each individual's characteristics, including race, gender, genetic factors, immune response variations. Recently, Quantitative and Systems Pharmacology (QSP) has been commonly used to discover, validate, and test drugs. QSP models are a system of differential equations that model the dynamic interactions between drug(s) and a biological system. These mathematical models provide an integrated ?systems level? approach to determining mechanisms of action of drugs and ?nding new ways to alter complex cellular networks with mono or combination therapy to obtain effective treatments. Since QSP models are a complex system of nonlinear equations with many unknown parameters, estimating the values of the model's parameters is extremely dif?cult. Existing parameter estimation methods for QSP models often use assembled data from various sources rather than a single curated dataset. These datasets are usually obtained through various biological experiments, in vitro and in vivo animal studies, thus rendering QSP models hard to be practicable for personalized treatments. To the best of our knowledge, no QSP model has been developed for personalized colon cancer treatments. In this project, we propose a unique approach to develop a data-driven QSP software to suggest effective treatment for each patient based on gene expression data from the primary tumor samples. Since signatures of main characteristics of tumors, such as immune response variations, can be found in gene expression pro?ling of primary tumors, we use gene expression data as input. We develop an innovative framework to systematically employ a combination of data science, mathematical, and statistical methods to obtain personalized colon cancer treatment. We employ novel inverse problem techniques to estimate the values of parameters of the model and statistical methods to perform sensitivity analysis. We will use these techniques to propose an optimal treatment strategy for each patient and predict the ef?cacy of the proposed treatment. The model might also suggest alternative therapies in case of low ef?cacy for some patients.